Speech Steganography in Wavelet Domain Using Continuous Genetic Algorithm

In this paper, we present a new adaptive steganography method using Lifting Wavelet Transform (LWT). In this method, we first calculate the LWT of the sample of host and secret speech signal. Then wavelet coefficients of secret speech signal will be fitted effectively and efficiently in host signal wavelet coefficients using continuous genetic algorithm. We used indirect replacement technique in 5 bits host using a proposed formula. Due to the quantization error, there are some differences between the secret signal before steganography and the extracted signal after steganography. However, these differences have an appropriate Gaussian noise model. We compress these differences using Huffman lossless compression method. The compression rate of such differences approach to the entropy, which is derived from Shannonʹs first theorem. Huffman lossless compression method, cause to small noise. We these compressed differences sent along the stego signal. The experimental results show that the proposed model has a statistical transparency higher than Least Significant Bit (LSB), Frequency Masking (FM) and Efficient Wavelet Masking (EWM) algorithms in time domain and frequency domain.